Submit an async finetune job
curl --request POST \
--url https://api.nixtla.io/v2/finetune/async \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"finetune_steps": 10,
"freq": "MS",
"model": "timegpt-1",
"series": {
"sizes": [
36
],
"y": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
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21,
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23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35
]
}
}
'import requests
url = "https://api.nixtla.io/v2/finetune/async"
payload = {
"finetune_steps": 10,
"freq": "MS",
"model": "timegpt-1",
"series": {
"sizes": [36],
"y": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
}
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
finetune_steps: 10,
freq: 'MS',
model: 'timegpt-1',
series: {
sizes: [36],
y: [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35
]
}
})
};
fetch('https://api.nixtla.io/v2/finetune/async', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.nixtla.io/v2/finetune/async",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'finetune_steps' => 10,
'freq' => 'MS',
'model' => 'timegpt-1',
'series' => [
'sizes' => [
36
],
'y' => [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35
]
]
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.nixtla.io/v2/finetune/async"
payload := strings.NewReader("{\n \"finetune_steps\": 10,\n \"freq\": \"MS\",\n \"model\": \"timegpt-1\",\n \"series\": {\n \"sizes\": [\n 36\n ],\n \"y\": [\n 0,\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n 10,\n 11,\n 12,\n 13,\n 14,\n 15,\n 16,\n 17,\n 18,\n 19,\n 20,\n 21,\n 22,\n 23,\n 24,\n 25,\n 26,\n 27,\n 28,\n 29,\n 30,\n 31,\n 32,\n 33,\n 34,\n 35\n ]\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.nixtla.io/v2/finetune/async")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"finetune_steps\": 10,\n \"freq\": \"MS\",\n \"model\": \"timegpt-1\",\n \"series\": {\n \"sizes\": [\n 36\n ],\n \"y\": [\n 0,\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n 10,\n 11,\n 12,\n 13,\n 14,\n 15,\n 16,\n 17,\n 18,\n 19,\n 20,\n 21,\n 22,\n 23,\n 24,\n 25,\n 26,\n 27,\n 28,\n 29,\n 30,\n 31,\n 32,\n 33,\n 34,\n 35\n ]\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.nixtla.io/v2/finetune/async")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"finetune_steps\": 10,\n \"freq\": \"MS\",\n \"model\": \"timegpt-1\",\n \"series\": {\n \"sizes\": [\n 36\n ],\n \"y\": [\n 0,\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n 10,\n 11,\n 12,\n 13,\n 14,\n 15,\n 16,\n 17,\n 18,\n 19,\n 20,\n 21,\n 22,\n 23,\n 24,\n 25,\n 26,\n 27,\n 28,\n 29,\n 30,\n 31,\n 32,\n 33,\n 34,\n 35\n ]\n }\n}"
response = http.request(request)
puts response.read_body{
"job_id": "fc-4f2a1c9e8b7d4a6f9c3e1b5d7a9f2c4e"
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>",
"input": "<unknown>",
"ctx": {}
}
]
}async jobs
Submit an async finetune job
Queues a finetune job and returns immediately with its job_id. The job runs in a sandbox; poll GET /v2/finetune/jobs/{job_id} for its state and result. Accepts the same body as the synchronous endpoint plus an optional job_options.
POST
/
v2
/
finetune
/
async
Submit an async finetune job
curl --request POST \
--url https://api.nixtla.io/v2/finetune/async \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"finetune_steps": 10,
"freq": "MS",
"model": "timegpt-1",
"series": {
"sizes": [
36
],
"y": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35
]
}
}
'import requests
url = "https://api.nixtla.io/v2/finetune/async"
payload = {
"finetune_steps": 10,
"freq": "MS",
"model": "timegpt-1",
"series": {
"sizes": [36],
"y": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
}
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
finetune_steps: 10,
freq: 'MS',
model: 'timegpt-1',
series: {
sizes: [36],
y: [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35
]
}
})
};
fetch('https://api.nixtla.io/v2/finetune/async', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.nixtla.io/v2/finetune/async",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'finetune_steps' => 10,
'freq' => 'MS',
'model' => 'timegpt-1',
'series' => [
'sizes' => [
36
],
'y' => [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35
]
]
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.nixtla.io/v2/finetune/async"
payload := strings.NewReader("{\n \"finetune_steps\": 10,\n \"freq\": \"MS\",\n \"model\": \"timegpt-1\",\n \"series\": {\n \"sizes\": [\n 36\n ],\n \"y\": [\n 0,\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n 10,\n 11,\n 12,\n 13,\n 14,\n 15,\n 16,\n 17,\n 18,\n 19,\n 20,\n 21,\n 22,\n 23,\n 24,\n 25,\n 26,\n 27,\n 28,\n 29,\n 30,\n 31,\n 32,\n 33,\n 34,\n 35\n ]\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.nixtla.io/v2/finetune/async")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"finetune_steps\": 10,\n \"freq\": \"MS\",\n \"model\": \"timegpt-1\",\n \"series\": {\n \"sizes\": [\n 36\n ],\n \"y\": [\n 0,\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n 10,\n 11,\n 12,\n 13,\n 14,\n 15,\n 16,\n 17,\n 18,\n 19,\n 20,\n 21,\n 22,\n 23,\n 24,\n 25,\n 26,\n 27,\n 28,\n 29,\n 30,\n 31,\n 32,\n 33,\n 34,\n 35\n ]\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.nixtla.io/v2/finetune/async")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"finetune_steps\": 10,\n \"freq\": \"MS\",\n \"model\": \"timegpt-1\",\n \"series\": {\n \"sizes\": [\n 36\n ],\n \"y\": [\n 0,\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n 10,\n 11,\n 12,\n 13,\n 14,\n 15,\n 16,\n 17,\n 18,\n 19,\n 20,\n 21,\n 22,\n 23,\n 24,\n 25,\n 26,\n 27,\n 28,\n 29,\n 30,\n 31,\n 32,\n 33,\n 34,\n 35\n ]\n }\n}"
response = http.request(request)
puts response.read_body{
"job_id": "fc-4f2a1c9e8b7d4a6f9c3e1b5d7a9f2c4e"
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>",
"input": "<unknown>",
"ctx": {}
}
]
}Authorizations
HTTPBearer
Body
application/json
Show child attributes
Show child attributes
The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.
Model to use as a string. Common options are (but not restricted to) timegpt-1 and timegpt-1-long-horizon. Full options vary by different users. Contact support@nixtla.io for more information. We recommend using timegpt-1-long-horizon for forecasting if you want to predict more than one seasonal period given the frequency of your data.
The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.
Required range:
x >= 0The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape', 'poisson']. It will only be used if finetune_steps is larger than 0. Default is a robust loss function that is less sensitive to outliers.
Available options:
default, mae, mse, rmse, mape, smape, poisson The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. Note that this parameter is only effective for timegpt-1 and timegpt-1-long-horizon models; it has no effect on the other models. By default, the value is set to 1.
Available options:
1, 2, 3, 4, 5 ID to assign to the finetuned model
Pattern:
^[a-zA-Z0-9\-_]{1,36}$ID of previously finetuned model
Pattern:
^[a-zA-Z0-9\-_]{1,36}$Zero-based indices of the exogenous features to treat as historical.
Required range:
x >= 0Compute multivariate predictions across a batch of multiple time series. Requires all time series with overlapping dates. Note that this is only effective for timegpt-2.1 model and it has no effect on the other models. By default, the value is set to False.
Optional dictionary of parameters to customize the behavior of the large time model.
Show child attributes
Show child attributes
Response
Successful Response
The 202 every async submit returns. Shared by all tasks — they differ in request, not reply.
Identifier for the accepted job. Prefixed per task (e.g. fc- for forecast).
Example:
"fc-4f2a1c9e8b7d4a6f9c3e1b5d7a9f2c4e"
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